Search Results
Modelling of sea salt concentrations over Europe: Key uncertainties and comparison with observations
Sea salt aerosol can significantly affect the air quality. Sea salt can cause enhanced concentrations of particulate matter and change particle chemical composition, in particular in coastal areas, and therefore should be accounted for in air quality modelling. We have used an EMEP Unified model to calculate sea salt concentrations and depositions over Europe, focusing on studying the effects of uncertainties in sea salt production and lifetime on calculation results. Model calculations of sea salt have been compared with EMEP observations of sodium concentrations in air and precipitation for a four year period, from 2004 to 2007, including size (fine/coarse) resolved EMEP intensive measurements in 2006 and 2007. In the presented calculations, sodium air concentrations are between 8% and 46% overestimated, whereas concentrations in precipitation are systematically underestimated by 65–70% for years 2004–2007. A series of model tests have been performed to investigate the reasons for this underestimation, but further studies are needed. The model is found to reproduce the spatial distribution of Na+ in air and precipitation over Europe fairly well, and to capture most of sea salt episodes. The paper presents the main findings from a series of tests in which we compare several different sea spray source functions and also look at the effects of meteorological input and the efficiency of removal processes on calculated sea salt concentrations. Finally, sea salt calculations with the EMEP model have been compared with results from the SILAM model and observations for 2007. While the models produce quite close results for Na+ at the majority of 26 measurement sites, discrepancies in terms of bias and temporal correlation are also found. Those differences are believed to occur due to differences in the representation of source function and size distribution of sea salt aerosol, different meteorology used for model runs and the different models' resolution. This study contributes to getting a better insight on uncertainties associated with sea salt calculations and thus facilitates further improvement of aerosol modelling on both regional and global scales.
Global gridded crop model evaluation: Benchmarking, skills, deficiencies and implications
Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.
Evaluation of a three-dimensional chemical transport model (PMCAMx) in the European domain during the EUCAARI May 2008 campaign
Sea salt aerosol can significantly affect the air quality. Sea salt can cause enhanced concentrations of particulate matter and change particle chemical composition, in particular in coastal areas, and therefore should be accounted for in air quality modelling. We have used an EMEP Unified model to calculate sea salt concentrations and depositions over Europe, focusing on studying the effects of uncertainties in sea salt production and lifetime on calculation results. Model calculations of sea salt have been compared with EMEP observations of sodium concentrations in air and precipitation for a four year period, from 2004 to 2007, including size (fine/coarse) resolved EMEP intensive measurements in 2006 and 2007. In the presented calculations, sodium air concentrations are between 8% and 46% overestimated, whereas concentrations in precipitation are systematically underestimated by 65–70% for years 2004–2007. A series of model tests have been performed to investigate the reasons for this underestimation, but further studies are needed. The model is found to reproduce the spatial distribution of Na+ in air and precipitation over Europe fairly well, and to capture most of sea salt episodes. The paper presents the main findings from a series of tests in which we compare several different sea spray source functions and also look at the effects of meteorological input and the efficiency of removal processes on calculated sea salt concentrations. Finally, sea salt calculations with the EMEP model have been compared with results from the SILAM model and observations for 2007. While the models produce quite close results for Na+ at the majority of 26 measurement sites, discrepancies in terms of bias and temporal correlation are also found. Those differences are believed to occur due to differences in the representation of source function and size distribution of sea salt aerosol, different meteorology used for model runs and the different models' resolution. This study contributes to getting a better insight on uncertainties associated with sea salt calculations and thus facilitates further improvement of aerosol modelling on both regional and global scales.
NCIO 1.0: A simple Fortran NetCDF interface
The NetCDF (Network Common Data Form) library has become an indispensable tool for data and model output management in geoscience. However for simple tasks, particularly in Fortran, the complexity of native NetCDF functionality can be cumbersome. The NCIO (NetCDF Input/Output) module has been designed as an interface to the NetCDF library with simplicity and ease of use in mind. While this implies that some NetCDF functionality is masked from the user, the subroutines provided here are adequate for basic serial reading and writing tasks of up to 6-D data arrays along with corresponding data attributes. The code is available online via a GitHub repository (http://www.github.com/alex-robinson/ncio), which includes an example program to illustrate the approach.
Model simulations of chemical effects of sprites in relation with observed HO2 enhancements over sprite-producing thunderstorms
Recently, measurements by the Superconducting Submillimeter-Wave Limb Emission Sounder (SMILES) satellite instrument have been presented which indicate an increase in mesospheric HO2 above sprite-producing thunderstorms. The aim of this paper is to compare these observations to model simulations of chemical sprite effects. A plasma chemistry model in combination with a vertical transport module was used to simulate the impact of a streamer discharge in the altitude range 70–80 km, corresponding to one of the observed sprite events. Additionally, a horizontal transport and dispersion model was used to simulate advection and expansion of the sprite air masses. The model simulations predict a production of hydrogen radicals mainly due to reactions of proton hydrates formed after the electrical discharge. The net effect is a conversion of water molecules into H+OH. This leads to increasing HO2 concentrations a few hours after the electric breakdown. Due to the modelled long-lasting increase in HO2 after a sprite discharge, an accumulation of HO2 produced by several sprites appears possible. However, the number of sprites needed to explain the observed HO2 enhancements is unrealistically large. At least for the lower measurement tangent heights, the production mechanism of HO2 predicted by the model might contribute to the observed enhancements.
Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)
The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past (1901-2009) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.